Rainfall is one of the most important meteorological phenomena since it provides water to the Earth's surface, which has a significant impact on the daily life of human beings. Gaining the knowledge of its behavior in a semi-arid basin is an important and challenging task to take advantage of this natural resource, given that water is usually scarce in such regions. Artificial intelligence and machine learning algorithms help to identify rainfall patterns and trends within a region. Multiple linear regression, random forest (RF), support vector machine, and artificial neural network (ANN) algorithms were implemented using daily rainfall data from climatological stations located within the basin using one station as a predictor variable and the remaining ones as input variables. The metrics to evaluate the model were the coefficient of determination (R2), mean absolute error, root mean square error, and the Kling–Gupta efficiency coefficient. The results showed that the daily rainfall prediction is better individually than overall, finding that the models obtained by RF and ANN simulate better daily rainfall in the basin.

Groundwater is the main source of freshwater available for human beings and is generally extracted through wells. The objective of this work was to schematize the groundwater flow systems within the Calera Aquifer through 3D geostatistical estimations of hydraulic head and physico-chemical parameters and the integration of hydrogeological features. The evolution of groundwater during its circulation in the subsoil can be done by identifying different types of flow (local, intermediate, regional, or mixed). Two main approaches have been proposed for the identification of flow systems: explaining the evolution of physico-chemical parameters of water through its interaction with the geologic medium, and using cluster analysis; however, these approaches usually do not consider simultaneously the 3D distribution of hydraulic head, water quality parameters, and the geological media that can be useful to delineate converging flow systems with a differentiated origin. In this paper, the determination of groundwater flow systems within the Calera aquifer in Mexico is supported with 3D representations of these hydrogeological variables besides constructive data of the sampled well. For the case study, the convergence of different flow systems that are not identified through a single cluster analysis was actually noticed by the proposal done in this paper.

In Mexico, agriculture in semi-arid regions is highly dependent on groundwater resources, where most of the aquifers’ characterization is a pending task. In particular, the depth to the basement is unknown for most of the Mexican territory. Hence, the development and performance of new techniques for the basement relief estimation is imperative for further hydrogeological studies. In this paper, we present a depth to the basement estimation using non-linear gravimetric inversion employing satellite data. Gravity forward modeling was implemented using both gravitational attraction due to juxtapositioned blocks and gravimetric non-linear inversion using conjugate gradient least squares to minimize the objective function in terms of a depth model. All of this took place under the sparse system framework. We present a synthetic result using the SEG-Bishop depth model taken for calibration purposes. Then, we recollected gravity data from The Satellite Geodesy group from SCRIPPS for the depth to the basement estimation of an unconfined aquifer in the northern-central semi-arid region of Zacatecas, Mexico. Both synthetic and satellite data were recovered, consistent depth models for both cases were presented, and a comparison with conventional gravimetric linear inversion for density estimation was performed.

This study presents a novel approach that integrates the Kalman filter and genetic algorithms to obtain the hydraulic parameters of a confined aquifer with precision, eliminating noise that is not normally considered in traditional procedures; these parameters are necessary for the design of wells, the calculation of water balances and the numerical modeling of aquifers. The Theis solution for horizontal radial groundwater flow to an extraction well within a confined aquifer is implemented in the Kalman filter to calibrate the hydraulic transmissivity and the storage coefficient, minimizing the differences between drawdown estimates and the Theis solution by means of genetic algorithms. The estimate error variances provided by the method allowed for the quantification of an approximate average drawdown measurement error of 0.12 m and 0.02 m, respectively, during the execution of two pumping tests.

Accurately modeling fractures in wave-propagation simulations is challenging due to their small scale relative to other features. While equivalent-media models can approximate fracture-induced anisotropy, they fail to capture their discrete influence on wave propagation. To address this limitation, the Interior-Penalty Discontinuous Galerkin Method (IP-DGM) can be adapted to incorporate the Linear-Slip Model (LSM) to represent fractures explicitly. In this study, we apply IP-DGM to elastic wave propagation in fractured cylindrical domains using realistic fracture compliances obtained from laboratory experiments (using ultrasonic-pulse transmission) to simulate the effects of fluid-filled fractures. We analyze how fracture spacing and fluid type influence P- and S-wave behavior, focusing on amplitude attenuation and wave-front delays. Our numerical results align with experimental and theoretical predictions, demonstrating that higher-density fluids enhance wave transmission, reducing the impedance contrast and improving coupling across fracture surfaces. These findings highlight the capability of IP-DGM to accurately model wave propagation in realistic fractured and saturated media, providing a valuable tool for seismic monitoring in fractured reservoirs and other applications where fluid-filled fractures are prevalent.

Groundwater characterization is seldom performed in countries with limited infrastructure and resources. A short-term solution is to use numerical simulation to study groundwater using the limited available data. We developed a GIS-integrated groundwater flow scheme based on the finite-difference method to numerically simulate flow using surface geological information. We performed groundwater simulations, using homogeneous and heterogeneous media to evaluate flow changes resulting from heterogeneities. The results show flow barriers in low hydraulic-conductivity zones that coincide with the administrative boundaries of the aquifer; however, in high hydraulic-conductivity zones, the administrative aquifer boundaries do not match the geological limits of the aquifer. This finding gives insight into reconsidering the boundaries of some aquifers in the region for their sustainability, with an integral understanding of groundwater.